Implementation of an Adapted Holonic Production Architecture

Gräßler, Iris; Pöhler, Alexander

doi:10.1016/j.procir.2017.03.176

Cited by 13 publications

(3 citation statements)

References 16 publications

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“…Another approach is related to an implementation framework of cloud-based holonic control architecture for CPPS (Quintanilla et al, 2017). In yet another study, an adaptive holonic architecture for distributed manufacturing control is developed (Gräßler and Pöhler, 2017).…”

Section: Holonic Paradigm As An Organizational Enablermentioning

confidence: 99%

A holonic framework for managing the sustainable supply chain in emerging economies with smart connected metabolism

Martín-Gómez

Aguayo-González

Luque

2019

Resources, Conservation and Recycling

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Section: Holonic Paradigm As An Organizational Enablermentioning

confidence: 99%

A holonic framework for managing the sustainable supply chain in emerging economies with smart connected metabolism

Martín-Gómez

Aguayo-González

Luque

2019

Resources, Conservation and Recycling

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“…The problem covers each cycle k concerning schedules of precedent cycles. In this problem, the objective function (1) minimizes the value between the production quantity of product i in period t in cycle k (Q k it ) related to the cycle k − 1.…”

Section: The Optimization Problemmentioning

confidence: 99%

“…As a result, conventional production planning and control models have been transformed into new flexible models that react dynamically during the production period. These models react dynamically to changes in scheduling, including disturbances arising from various factors such as operating machinery, production expansion, processes, products, and production volumes [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Reducing Nervousness in Master Production Planning: A Systematic Approach Incorporating Product-Driven Strategies

2023

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Manufacturing companies face a significant challenge when developing their master production schedule, navigating unforeseen disruptions during daily operations. Moreover, fluctuations in demand pose a substantial risk to scheduling and are the main cause of instability and uncertainty in the system. To address these challenges, employing flexible systems to mitigate uncertainty without incurring additional costs and generate sustainable responses in industrial applications is crucial. This paper proposes a product-driven system to complement the master production plan generated by a mathematical model. This system incorporates intelligent agents that make production decisions with a function capable of reducing uncertainty without significantly increasing production costs. The agents modify or determine the forecasted production quantities for each cycle or period. In the case study conducted, a master production plan was established for 12 products over a one-year time horizon. The proposed solution achieved an 11.42% reduction in uncertainty, albeit with a 2.39% cost increase.

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